It has been just over two years since we introduced the Elastic Common Schema (ECS), and what a journey it’s been. From categorization fields to request for comments to Threat Intelligence fields, ECS has evolved rapidly over the course of the last two years. In this blog post, I would like to reflect on the ECS journey so far, and look towards the future of ECS.

I’ll come clean and admit it – this part of the series will be a bit interesting given the fact that I know very little about Elasticsearch. So really, this is an honest test of the question – “can I still build something good with Dashboard Server even if I only have nominal knowledge of the tool where the data is sourced from?”

Elastic's new frozen data tier decouples compute from storage and leverages low-cost object stores such as Google Cloud Storage, Azure Blob Storage, or Amazon S3 to directly power searches. It provides unlimited scaling of storage while preserving the ability to efficiently query the data without any need to rehydrate it first, making it easier and cheaper to manage data at scale.

Hiya! With Elastic’s expansion of our Elasticsearch Service Cloud offering and automated onboarding, we’ve expanded the Elastic Stack audience from full ops teams to data engineers, security teams, and consultants. As an Elastic support rep, I’ve enjoyed interacting with more user backgrounds and with even wider use cases.

On the 14th of January 2021, Elasticsearch B.V. announced that future releases of Elasticsearch and Kibana would be released under a dual license SSPL (Server Side Public License). As a result of this change it is evident that the components that make up Elasticsearch and Kibana in version 7.11 (and onwards) of the ELK Stack will no longer be considered as open source based upon the Open Source Initiative's requirements for licensing.

Today, I’m excited to officially announce our support for the OpenSearch project, the new fork of the Elasticsearch and Kibana codebases. As we previously shared, Logz.io has the utmost commitment to its customers and the community to ensure that these open-source technologies will prosper by being built for the community and guided by the community.

Have you ever tried to search for objects in images? Elasticsearch can help you store, analyze, and search for objects in images or videos. In this quick tutorial, we’ll show you how to build a system for facial recognition with Python. Learn more about how to detect and encode facial information — and find matches in search.